{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "x0  shape:  (2, 5)\n",
      "x0  content:[[0 0 0 0 0]\n",
      " [0 0 0 0 0]]\n",
      "\n",
      "x1  shape:(5, 2)\n",
      "x1  content:[[1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np  #引入numpy数组\n",
    "\n",
    "x0  =  np.zeros((2,5),dtype=int)#  构建  2行  5列  整型  numpy  全0数组  x0\n",
    "x1  =  np.ones((5,2),dtype=int)\n",
    "#  构建  5行  2列  整型  numpy  全1数组  x1\n",
    "\n",
    "#  构建结果多行字符串\n",
    "#  针对x0,x1打印其结构和完整内容\n",
    "info=f'''\n",
    "x0  shape:  {x0.shape}\n",
    "x0  content:{x0}\n",
    "\n",
    "x1  shape:{x1.shape}\n",
    "x1  content:{x1}\n",
    "'''\n",
    "print(info) #打印字符串内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  引入合适的模块\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "x  =  np.eye(100)\n",
    "#  构建一个  100*100  的对角线为1其余为0的numpy数组\n",
    "#  用plt绘制x对应的图像\n",
    "plt.imshow(x,cmap='gray')\n",
    "#  色彩模式为:  灰白\n",
    "\n",
    "#  plt.show()  测试代码不能直接显示\n",
    "#  请把plt.show()的结果保存到  out.png  中\n",
    "#  要求使用bbox_inches属性实现显示结果尽量少空白内容\n",
    "plt.savefig('out.png', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  引入合适的模块\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "#  生成等差数列  numpy  数组\n",
    "#  范围:  [0,255]\n",
    "#  步长:  5\n",
    "x  =  np.arange(0,256,5)\n",
    "\n",
    "\n",
    "#  用plt绘制x对应的图像\n",
    "#  注意  x  为一维列表无法显示成图像\n",
    "#  所以要把x转化为一个二维列表:  [x]即可\n",
    "plt.imshow([x],cmap='gray')\n",
    "  #  色彩模式为:  灰白\n",
    "plt.axis('off')\n",
    "  #  关闭图像的所有数值标尺\n",
    "\n",
    "#  plt.show()  测试代码不能直接显示\n",
    "#  请把plt.show()的结果保存到  out.png  中\n",
    "#  要求使用bbox_inches属性实现显示结果尽量少空白内容\n",
    "plt.savefig('out.png',bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  引入合适的模块\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "#  此句话限制在不同平台生成随机数相同\n",
    "#  以便之后答案的审核\n",
    "np.random.seed(2021)  \n",
    "\n",
    "size=100  #  此变量控制之后要生成多少个数\n",
    "\n",
    "#  设置x轴为等差数列\n",
    "#  范围:  [0,50]\n",
    "#  包含数字个数:  size\n",
    "x=np.linspace(0,50,size)\n",
    "\n",
    "y1=np.random.randn(size)\n",
    "  #生成size个满足标准正太分布的数,  存入y1\n",
    "y2=np.random.rand(size)\n",
    "  #  生成size个介于0和1的，均匀分布的随机数,  存入y2\n",
    "\n",
    "\n",
    "plt.plot(x,y1)  #  画出一个  x  ->  y1  一一对应的折线图\n",
    "plt.plot(x,y2)  #  画出一个  x  ->  y2  一一对应的折线图\n",
    "\n",
    "#  plt.show()  测试代码不能直接显示\n",
    "#  请把plt.show()的结果保存到  out.png  中\n",
    "#  要求使用bbox_inches属性实现显示结果尽量少空白内容\n",
    "y1=plt.savefig('out.png',bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  引入合适的模块\n",
    "import numpy as np\n",
    "from numpy.random import randint\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "#  此句话限制在不同平台生成随机数相同\n",
    "#  以便之后答案的审核\n",
    "np.random.seed(2021)  \n",
    "\n",
    "#  绘制100*100的随机整数numpy数组\n",
    "#  每一个整数的范围是:  [0,255]\n",
    "x  =  randint(0,256,size=(100,100))\n",
    "\n",
    "\n",
    "#  用plt把x的图像显示出来\n",
    "plt.imshow(x,cmap='gray',vmin=0,vmax=255)  #色彩模式为:  灰白 最小最大值设置为0和255\n",
    "plt.axis('off')\n",
    "#  关闭图像的所有数值标尺\n",
    "\n",
    "#  plt.show()  测试代码不能直接显示\n",
    "#  请把plt.show()的结果保存到  out.png  中\n",
    "#  要求使用bbox_inches属性实现显示结果尽量少空白内容\n",
    "plt.savefig(\"out.png\",bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-95484c339364>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m   \u001b[0;31m#  用opencv读取  dog.jpg  的灰度图形式\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mimg\u001b[0m  \u001b[0;34m=\u001b[0m  \u001b[0mimg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m600\u001b[0m\u001b[0;34m]\u001b[0m    \u001b[0;31m#  截取图像的  高[500,1100)  宽[100:600)部分\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mw\u001b[0m  \u001b[0;34m=\u001b[0m  \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "#  引入合适的模块\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "#  此句话限制在不同平台生成随机数相同\n",
    "#  以便之后答案的审核\n",
    "np.random.seed(2021)  \n",
    "\n",
    "img  =  cv.imread(\"dog.jpg\",0)\n",
    "  #  用opencv读取  dog.jpg  的灰度图形式\n",
    "\n",
    "img  =  img[500:1100,100:600]    #  截取图像的  高[500,1100)  宽[100:600)部分\n",
    "\n",
    "h,  w  =  img.shape\n",
    "  #  获取图像的高度->h,  和宽度->w\n",
    "\n",
    "noise  =  np.random.randn(h,w)*20  #  生成高h宽w的正太分布随机值数组，并把每一个值都放大20倍  ->  生成噪音数组  noise\n",
    "\n",
    "img_noisy  =  img  +  noise\n",
    "#  把原图  +  噪音  \n",
    "\n",
    "plt.imshow(img_noisy,cmap='gray')\n",
    "  #  以灰白色绘制img_noisy的灰度图\n",
    "\n",
    "#  plt.show()  测试代码不能直接显示\n",
    "#  请把plt.show()的结果保存到  out.png  中\n",
    "#  要求使用bbox_inches属性实现显示结果尽量少空白内容\n",
    "plt.savefig(\"out.png\",bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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